Why Discernment Matters Even More In The Age Of AI

Marketing as a discipline continues to evolve at a breakneck pace, but the way we research it online often hasn’t kept up. Every search yields pages of articles, guides, and expert opinions, but not all of them come from people who actively practice marketing or understand the complexity of real campaigns. Today’s challenge isn’t the lack of information; it’s discerning whether that information reflects lived experience or echoes what already exists online.

When I created Martech Zone, I built it as a practical resource—a place to document real experiments, share what worked, admit what didn’t, and help marketers make informed decisions without hype or inflated claims. With AI now producing more content than humans ever could, the need for this kind of validation has only intensified. I’m an enormous advocate of AI, but using AI responsibly requires a clear understanding of what it can and cannot do. And when it comes to guidance, AI can explain concepts beautifully, but it cannot have performed the work behind them.

The Qualities I Value in Marketing Publications

High-quality marketing resources combine expertise, transparency, and actionability. They do more than explain—they demonstrate. The best writers in this space apply what they teach and share the results, allowing readers to see how ideas translate into practice.

I value publications where the authors practice what they advocate. Recommendations are grounded in campaigns that were actually executed, not simulations or theories. When someone shows the setup, the data, the adjustments, and the outcomes, the advice becomes far more credible and valuable.

I also appreciate marketing resources that challenge their own assumptions. Industry statistics are everywhere, but numbers often reflect the methodology, sample, or incentives of the organization producing them. Responsible writers compare multiple sources, present contradictory evidence, and avoid framing a single data point as a universal truth. This is essential because marketers make significant decisions based on these insights.

Another hallmark of valuable resources is the willingness to equip marketers with actionable steps. It’s not enough to say what worked. A strong publication provides tools, examples, frameworks, and instructions that enable others to try similar approaches and measure their own performance.

And finally, the smartest publications invite conversation and even welcome disagreement. Marketing is far too nuanced—and far too context-dependent—for any one person or group to have all the answers.

The Warning Signs That a Resource Lacks Real Expertise

Just as strong publications stand out, specific patterns reveal when a resource might be more focused on impressions and search visibility than on helping professionals.

1. Observation Without Practice: When a resource only summarizes trends or repeats what others have written, it signals a lack of real-world experience. Insight becomes noise when it isn’t backed by application, experimentation, or firsthand understanding, and marketers deserve guidance from people who have actually faced the challenges they describe.
2. Writing That Ignores Marketers’ Needs: Content aimed at a broad audience often lacks the depth needed for strategic execution. True marketing advice speaks directly to practitioners by offering clarity on how to implement, test, adapt, and refine ideas—turning abstract concepts into actions that drive results.
3. Expert-Centric Narratives: A resource becomes less credible when it focuses more on elevating the author than on helping the reader. Practical marketing guidance prioritizes the customer, the process, the tools, and the outcomes. Real practitioners emphasize the work itself rather than building a persona around their expertise.

Why AI Makes Validation Even More Important

AI has introduced incredible advantages into the marketing profession. It accelerates research, distills complexity, and surfaces patterns we might not notice. It’s a force multiplier for creativity and efficiency. I rely on it often.

But AI cannot validate advice through practice. It has no campaigns to point to, no clients it has guided, no unpredictable human behavior it has navigated, and no unexpected failures it has had to troubleshoot. Its output is based on patterns, not experience.

This doesn’t make AI unreliable—it makes AI incomplete. The danger arises when readers assume AI-created content is grounded in real execution. Without human validation, AI can unintentionally reinforce outdated tactics, bias, or oversimplified conclusions. And as AI-generated (GenAI) content continues to grow, the signal-to-noise ratio in marketing publications is becoming increasingly challenging by the day.

That’s why practitioner-led resources matter more than ever. They provide the context AI cannot. They show nuance that AI cannot infer. They capture the lived lessons, exceptions, and hard-earned wisdom that AI cannot experience.

Why This Matters for Every Marketer

Marketing is a discipline shaped by real customers, unpredictable behavior, platform quirks, shifting algorithms, and organizational constraints. Marketers operate within budget limits, time pressures, internal politics, uneven talent across teams, imperfect data, and competing priorities. None of that is captured in surface-level content, generic frameworks, or theoretical guidance, which is why every marketer must carefully evaluate the credibility of the resources they rely on.

And now there is an additional layer to navigate: GenAI advice. I advocate for AI because it accelerates research, clarifies complexity, and enhances creativity, but it does not understand your actual environment. If you prompt AI for the best subject line, the request will likely not consider your team’s skill level, your budget, your technical stack, your brand voice, your customer segments, your lifecycle stage, your offer, your past performance, your timing, your calendar, your internal approval process, your list quality, your compliance requirements, your inbox reputation, or a dozen other variables that influence performance. Since most prompts do not include these realities, the model cannot infer them with any statistical reliability.

This creates a significant risk: AI can produce confident, polished recommendations that are directionally helpful but contextually wrong. Without practitioner oversight, marketers may unknowingly apply advice that isn’t aligned with their constraints or opportunities.

That’s why discernment is more important than ever. Whether the guidance comes from a major publication, a small site, or an AI model, marketers must ask the same questions:

• Was this validated in real practice?
• Does it reflect conditions similar to mine?
• Is it actionable, repeatable, and transparent?
• Does it account for the messy, imperfect realities of actual marketing environments?

The highest-value marketing resources—human or AI-assisted—are those created by practitioners who understand that real-world execution is complex, nuanced, and rarely tidy. As AI amplifies the volume of content online, the responsibility shifts to marketers to verify the advice they consume and ensure it aligns with their context, not a hypothetical one generated from statistical averages.